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Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-ggdmc 0.2.6.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrixstats@1.5.0 r-ggplot2@4.0.1 r-data-table@1.17.8 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/yxlin/ggdmc
Licenses: GPL 2
Build system: r
Synopsis: Cognitive Models
Description:

Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo.

r-geofis 1.1.1
Dependencies: mpfr@4.2.2 gmp@6.3.0
Propagated dependencies: r-sp@2.2-0 r-sf@1.0-23 r-rdpack@2.6.4 r-rcpp@1.1.0 r-r6@2.6.1 r-purrr@1.2.0 r-nnls@1.6 r-magrittr@2.0.4 r-itertools@0.1-3 r-foreach@1.5.2 r-fispro@1.1.4 r-data-tree@1.2.0 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://www.geofis.org
Licenses: CeCILL
Build system: r
Synopsis: Spatial Data Processing for Decision Making
Description:

This package provides methods for processing spatial data for decision-making. This package is an R implementation of methods provided by the open source software GeoFIS <https://www.geofis.org> (Leroux et al. 2018) <doi:10.3390/agriculture8060073>. The main functionalities are the management zone delineation (Pedroso et al. 2010) <doi:10.1016/j.compag.2009.10.007> and data aggregation (Mora-Herrera et al. 2020) <doi:10.1016/j.compag.2020.105624>.

r-gbs2ploidy 1.0
Propagated dependencies: r-rjags@4-17 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gbs2ploidy
Licenses: GPL 3
Build system: r
Synopsis: Inference of Ploidy from (Genotyping-by-Sequencing) GBS Data
Description:

This package provides functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.

r-gallo 1.5
Propagated dependencies: r-webshot@0.5.5 r-visnetwork@2.1.4 r-unbalhaar@2.1 r-stringr@1.6.0 r-rtracklayer@1.70.0 r-rcolorbrewer@1.1-3 r-matrix@1.7-4 r-lattice@0.22-7 r-igraph@2.2.1 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dt@0.34.0 r-dplyr@1.1.4 r-doparallel@1.0.17 r-data-table@1.17.8 r-compquadform@1.4.4 r-circlize@0.4.16
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: <https://github.com/pablobio/GALLO>
Licenses: GPL 3
Build system: r
Synopsis: Genomic Annotation in Livestock for Positional Candidate LOci
Description:

The accurate annotation of genes and Quantitative Trait Loci (QTLs) located within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a crucial step the most common genomic analyses performed in livestock, such as Genome-Wide Association Studies or transcriptomics. The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others.

r-gformula 1.1.1
Propagated dependencies: r-truncreg@0.2-5 r-truncnorm@1.0-9 r-survival@3.8-3 r-stringr@1.6.0 r-progress@1.2.3 r-nnet@7.3-20 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/CausalInference/gfoRmula
Licenses: GPL 3
Build system: r
Synopsis: Parametric G-Formula
Description:

This package implements the non-iterative conditional expectation (NICE) algorithm of the g-formula algorithm (Robins (1986) <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). The g-formula can estimate an outcome's counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders. This package can be used for discrete or continuous time-varying treatments and for failure time outcomes or continuous/binary end of follow-up outcomes. The package can handle a random measurement/visit process and a priori knowledge of the data structure, as well as censoring (e.g., by loss to follow-up) and two options for handling competing events for failure time outcomes. Interventions can be flexibly specified, both as interventions on a single treatment or as joint interventions on multiple treatments. See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on how to use the package.

r-gslnls 1.4.2
Dependencies: gsl@2.8 gsl@2.8
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/JorisChau/gslnls
Licenses: LGPL 3
Build system: r
Synopsis: GSL Multi-Start Nonlinear Least-Squares Fitting
Description:

An R interface to weighted nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquardt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Multi-start optimization based on quasi-random samples is implemented using a modified version of the algorithm in Hickernell and Yuan (1997, OR Transactions). Robust nonlinear regression can be performed using various robust loss functions, in which case the optimization problem is solved by iterative reweighted least squares (IRLS). Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class.

r-gsynth 1.3.1
Propagated dependencies: r-panelview@1.1.18 r-ggplot2@4.0.1 r-fect@2.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://yiqingxu.org/packages/gsynth/
Licenses: Expat
Build system: r
Synopsis: Generalized Synthetic Control Method
Description:

Conducts causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability.

r-grpreg 3.5.0
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://pbreheny.github.io/grpreg/
Licenses: GPL 3
Build system: r
Synopsis: Regularization Paths for Regression Models with Grouped Covariates
Description:

Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. For more information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>, Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015) <doi:10.1111/biom.12300>, or visit the package homepage <https://pbreheny.github.io/grpreg/>.

r-growr 1.3.0
Propagated dependencies: r-rlang@1.1.6 r-rdpack@2.6.4 r-r6@2.6.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/kuadrat/growR
Licenses: Expat
Build system: r
Synopsis: Implementation of the Vegetation Model ModVege
Description:

Run grass growth simulations using a grass growth model based on ModVege (Jouven, M., P. Carrère, and R. Baumont "Model Predicting Dynamics of Biomass, Structure and Digestibility of Herbage in Managed Permanent Pastures. 1. Model Description." (2006) <doi:10.1111/j.1365-2494.2006.00515.x>). The implementation in this package contains a few additions to the above cited version of ModVege, such as simulations of management decisions, and influences of snow cover. As such, the model is fit to simulate grass growth in mountainous regions, such as the Swiss Alps. The package also contains routines for calibrating the model and helpful tools for analysing model outputs and performance.

r-ginivarci 0.0.1-3
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=giniVarCI
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Gini Indices, Variances and Confidence Intervals for Finite and Infinite Populations
Description:

Estimates the Gini index and computes variances and confidence intervals for finite and infinite populations, using different methods; also computes Gini index for continuous probability distributions, draws samples from continuous probability distributions with Gini indices set by the user; uses Rcpp'. References: Muñoz et al. (2023) <doi:10.1177/00491241231176847>. à lvarez et al. (2021) <doi:10.3390/math9243252>. Giorgi and Gigliarano (2017) <doi:10.1111/joes.12185>. Langel and Tillé (2013) <doi:10.1111/j.1467-985X.2012.01048.x>.

r-groupica 0.1.1
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/sweichwald/groupICA-R
Licenses: AGPL 3
Build system: r
Synopsis: Independent Component Analysis for Grouped Data
Description:

This package contains an implementation of an independent component analysis (ICA) for grouped data. The main function groupICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding for user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/groupICA/>.

r-gencor 1.0.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gencor
Licenses: GPL 3
Build system: r
Synopsis: Generate Customized Correlation Matrices
Description:

This package provides a function that generates a customized correlation matrix based on limit values and proportions for intervals composed by its limits. It can also generate random matrices with low, medium, and high correlations, in which low, medium, and high thresholds are user-defined.

r-geysertimes 0.1.9
Propagated dependencies: r-readr@2.1.6 r-lubridate@1.9.4 r-jsonlite@2.0.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/geysertimes/geysertimes-r-package
Licenses: Expat
Build system: r
Synopsis: Geyser Data from GeyserTimes.org
Description:

Download geyser eruption and observation data from the GeyserTimes site (<https://geysertimes.org>) and optionally store it locally. The vignette shows a simple analysis of downloading, accessing, and summarizing the data.

r-geostatsp 2.0.8
Propagated dependencies: r-terra@1.8-86 r-numderiv@2016.8-1.1 r-matrix@1.7-4 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=geostatsp
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Geostatistical Modelling with Likelihood and Bayes
Description:

Geostatistical modelling facilities using SpatRaster and SpatVector objects are provided. Non-Gaussian models are fit using INLA', and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The RandomFields package is available at <https://www.wim.uni-mannheim.de/schlather/publications/software>.

r-gdpuc 1.6.1
Propagated dependencies: r-withr@3.0.2 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-glue@1.8.0 r-dplyr@1.1.4 r-crayon@1.5.3 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/pik-piam/GDPuc
Licenses: GPL 3+
Build system: r
Synopsis: Easily Convert GDP Data
Description:

Convert GDP time series data from one unit to another. All common GDP units are included, i.e. current and constant local currency units, US$ via market exchange rates and international dollars via purchasing power parities.

r-greeks 1.5.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-shiny@1.11.1 r-rcpp@1.1.0 r-plotly@4.11.0 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-dqrng@0.4.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/ahudde/greeks
Licenses: Expat
Build system: r
Synopsis: Sensitivities of Prices of Financial Options and Implied Volatilities
Description:

This package provides methods to calculate sensitivities of financial option prices for European, geometric and arithmetic Asian, and American options, with various payoff functions in the Black Scholes model, and in more general jump diffusion models. A shiny app to interactively plot the results is included. Furthermore, methods to compute implied volatilities are provided for a wide range of option types and custom payoff functions. Classical formulas are implemented for European options in the Black Scholes Model, as is presented in Hull, J. C. (2017), Options, Futures, and Other Derivatives. In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential Lévy processes. <doi:10.1007/s11009-023-10014-5>. For American options, the Binomial Tree Method is implemented, as is presented in Hull, J. C. (2017).

r-gmfamm 0.1.0
Propagated dependencies: r-mgcv@1.9-4 r-matrix@1.7-4 r-mass@7.3-65 r-bamlss@1.2-5
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gmfamm
Licenses: GPL 3
Build system: r
Synopsis: Generalized Multivariate Functional Additive Models
Description:

Supply implementation to model generalized multivariate functional data using Bayesian additive mixed models of R package bamlss via a latent Gaussian process (see Umlauf, Klein, Zeileis (2018) <doi:10.1080/10618600.2017.1407325>).

r-getdesigns 1.2.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GETdesigns
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Extended Triangular Designs ('GETdesigns')
Description:

Since their introduction by Bose and Nair (1939) <https://www.jstor.org/stable/40383923>, partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) <doi:10.1080/01621459.1952.10501161> for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,â ¦,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs.

r-gd 10.8
Propagated dependencies: r-bammtools@2.1.12
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/ausgis/GD
Licenses: GPL 3
Build system: r
Synopsis: Geographical Detectors for Assessing Spatial Factors
Description:

Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.

r-glmsdata 1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GLMsData
Licenses: GPL 2+
Build system: r
Synopsis: Generalized Linear Model Data Sets
Description:

Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.

r-gtes 1.0.0
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrixstats@1.5.0 r-matrix@1.7-4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/yzhou1999/GTEs
Licenses: GPL 3
Build system: r
Synopsis: Group Technical Effects
Description:

Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects.

r-gcdnet 1.0.6
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/emeryyi/gcdnet
Licenses: GPL 2+
Build system: r
Synopsis: The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm
Description:

This package implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.

r-gptstudio 0.4.0
Propagated dependencies: r-yaml@2.3.10 r-waiter@0.2.5-1.927501b r-stringr@1.6.0 r-sseparser@0.1.0 r-shiny-i18n@0.3.0 r-shiny@1.11.1 r-rvest@1.0.5 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-r6@2.6.1 r-purrr@1.2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-ids@1.0.1 r-httr2@1.2.1 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-glue@1.8.0 r-fontawesome@0.5.3 r-curl@7.0.0 r-colorspace@2.1-2 r-cli@3.6.5 r-bslib@0.9.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/MichelNivard/gptstudio
Licenses: Expat
Build system: r
Synopsis: Use Large Language Models Directly in your Development Environment
Description:

Large language models are readily accessible via API. This package lowers the barrier to use the API inside of your development environment. For more on the API, see <https://platform.openai.com/docs/introduction>.

r-gammafuncmodel 6.0
Propagated dependencies: r-scales@1.4.0 r-rootsolve@1.8.2.4 r-rlang@1.1.6 r-rdpack@2.6.4 r-patchwork@1.3.2 r-nlme@3.1-168 r-gridextra@2.3 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-dplyr@1.1.4 r-cubature@2.1.4-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gammaFuncModel
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Non-Linear Mixed Effects Model Based on the Gamma Function Form
Description:

Identifies biomarkers that exhibit differential response dynamics by time across groups and estimates kinetic properties of biomarkers.

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Total results: 68655